Abstract
Since the hippocampus is of small size, low contrast, and irregular shape, a novel hippocampus segmentation method based on subspace patch-sparsity clustering in brain MRI is proposed to improve the segmentation accuracy, which requires that the representation coefficients in different subspaces should be as sparse as possible, while the representation coefficients in the same subspace should be as average as possible. By restraining the coefficient matrix with the patch-sparse constraint, the coefficient matrix contains a patch-sparse structure, which is helpful to the hippocampus segmentation. The experimental results show that our proposed method is effective in the noisy brain MRI data, which can well deal with hippocampus segmentation problem.
Highlights
By restraining the coefficient matrix with the block-sparse constraint, the coefficient matrix contains a patch-sparse structure, which is helpful to the hippocampus segmentation. e experimental results show that our proposed method is effective to the noisy brain MRI data, which can well deal with the hippocampus segmentation problem
In order to better show the advantages of the improved sparse subspace clustering segmentation method, this paper selects simulated brain MRI and real brain MRI data for simulation analysis. e real brain MRI data is 30 slices of 3D T1WI MRI images of adult male head with 3 mm slice provided by the Department of Radiology, West China Medical University. e simulation brain MRI is from the database BrainWeb, where BrainWeb provides standard segmentation results and is convenient for quantitative evaluation
In order to qualitatively evaluate the performance of all the comparison algorithms, we use false negative ratio (FNR), ratio of segmentation error (RSE), and dice similarity coefficient (DSC) to measure the segmentation accuracy
Summary
In the subspace clustering process, the data is regarded as the vertices in the graph, the weight matrix or adjacency matrix of the graph is obtained by solving the representation coefficient of the data in the subspace, and the final clustering result is obtained by using the spectral clustering method. E sparse representation of signal can be achieved by constructing appropriate dictionary and sparsity requirement of coefficients. In order to reveal the more essential characteristics and get a more sparse representation of the signal, many scholars have done a lot of work in the construction of dictionaries. Erefore, as for high-dimensional data, the representation coefficients of data in low-dimensional subspace are sparse. A suitable Ω(Z) is designed so that the matrix Z obtained from the model satisfies the properties of sparsity between classes and consistency within classes
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